Aggregating different image features for image retrieval has recently shown its effectiveness. While highly effective, though, the question of how to uplift the impact of the best features for a specific query image persists as an open computer vision problem. In this paper, we propose a computationally efficient approach to fuse several hand-crafted and deep features, based on the probabilistic distribution of a given membership score of a constrained cluster in an unsupervised manner. First, we introduce an incremental nearest neighbor (NN) selection method, whereby we dynamically select k-NN to the query. We then build several graphs from the obtained NN sets and employ constrained dominant sets (CDS) on each graph G to assign edge weights which consider the intrinsic manifold structure of the graph, and detect false matches to the query. Finally, we elaborate the computation of feature positive-impact weight (PIW) based on the dispersive degree of the characteristics vector. To this end, we exploit the entropy of a cluster membership-score distribution. In addition, the final NN set bypasses a heuristic voting scheme. Experiments on several retrieval benchmark datasets show that our method can improve the state-of-the-art result.

聚集不同的图像特征以进行图像检索最近已显示出其有效性。虽然非常有效，但是如何提高最佳功能对特定查询图像的影响这一问题仍然存在，是一个开放的计算机视觉问题。在本文中，我们提出了一种计算有效的方法，以无监督的方式基于给定约束簇的隶属度分数的概率分布，融合了几个手工制作的深度特征。首先，我们介绍一种增量式最近邻（NN）选择方法，从而动态地为查询选择k-NN。然后，我们从获得的NN集构建几个图，并在每个图G上使用约束优势集（CDS）来分配考虑图的固有流形结构的边缘权重，并检测对查询的错误匹配。最后，基于特征向量的分散程度，阐述了特征正影响权重（PIW）的计算。为此，我们利用了集群成员评分分布的熵。此外，最终的NN集会绕过启发式投票方案。在多个检索基准数据集上的实验表明，我们的方法可以改善最新的结果。